Atrial fibrillation (AF) is the most common abnormal heart rhythm, especially in seniors, with irregular and erratic cardiac patterns. Usually, surface ECG signal analysis based on waveform morphology and time domain parameters are utilized for cardiac AF rhythm detection and characterization, such as P wave morphology changes, R-R-wave time interval, heart rate variability analysis, etc. However, the waveform morphologies and time domain parameter analysis are sometimes subjective and time-consuming, and require extensive expertise and clinical experience for accurate pathology interpretation and proper cardiac rhythm management. Some sophisticated mathematical theories have been applied to biomedical signal interpretation, for example, frequency analysis (such as dominant frequency analysis, frequency singularity, etc), wavelet decomposition analysis, statistical analysis (such as autocorrelation analysis, coherence analysis, etc), and nonlinear entropy evaluation. Nevertheless, these applications focus on generating a new pathology index for qualitative cardiac AF rhythm identification. Until now no well-accepted methods and algorithms for qualitative and quantitative characterizing AF, especially the severity quantification of AF pathology, have been developed. Several shortcomings with current clinical investigation and diagnosis strategies for atrial fibrillation exist. For example:
1. Clinical real time and accurate AF diagnosis and characterization need extensive clinical experience and medial expertise for precise and correct cardiac electrophysiological signal interpretation. This usually increases the clinical training cost and the complexity involved in diagnosing a condition.
2. Time domain parameter based analysis, such as R-R-wave interval and heart rate variability (HRV), is utilized for qualitative diagnosis and detection of AF. However, these methods do not accurately characterize the pathological severity of the AF pathology.
3. Clinically, most AF diagnosis and evaluation are based on the whole heart beat. This includes P wave, QRS complex T-wave, etc. analysis. Some analysis and filtering algorithms (frequency analysis) employ the whole heart beat for AF malfunction pattern recognition and characterization. This unavoidably increases the risk of signal/analysis distortion caused by noise and artifacts.
4. Algorithm simplicity, accuracy and ease of use are hurdles that need to be overcome with current AF analysis and characterization. For example heart rate variability is utilized for AF recognition. However, there are extensive statistical evaluations on the variability threshold which may be different from patient to patient. These kinds of medical diagnosis factors may present more application complexities to the user. Furthermore, some of current AF research methods and applications are not stable, time consuming and have a high error rate (false alarm risk).
The need exists for more reliably and precisely identifying the cardiac disorders, differentiating cardiac arrhythmias, characterizing the pathological severities, and even prediction of the life-threatening events. A system according to invention principles addresses these deficiencies and related problems.